costs 0.1.48

Zero-config AI cost calculator per commit/model with liteLLM

AI Cost Tracker

AI Cost Tracking

• 🤖 LLM usage: $0.8077 (47 commits)
• 👤 Human dev: ~$621 (6.2h @ $100/h, 30min dedup)

Generated on 2026-03-29 using openrouter/qwen/qwen3-coder-next

---

Track AI usage costs across your git commits with three flexible usage modes - no initial configuration required.

Features

• liteLLM Integration - Support for 100+ AI providers via liteLLM
• Default: Qwen3 Coder Next - Pre-configured with openrouter/qwen/qwen3-coder-next
• Zero Config - Works out of the box, reads from .env file
• Smart Token Estimation - Accurate cost calculation using tiktoken (OpenAI) and Anthropic tokenizer
• ROI Calculation - Track value generated vs AI costs
• Date Filtering - Analyze specific days, date ranges, or full history
• Auto Badges - Automatically generate and update cost badges in README
• Rich Reports - Markdown and HTML reports with visualizations
• All Commits Support - Analyze all commits with --all flag (not just AI-tagged)

Tokenization

The tool uses accurate tokenization for precise cost estimation:

Model	Tokenizer	Accuracy
OpenAI GPT-4/4o	tiktoken (cl100k_base)	~99%
Anthropic Claude	Anthropic SDK (local)	100%
OpenRouter/Other	tiktoken fallback	~95%

Token Counting Examples

from costs.tokenizers import count_tokens, Tokenizer

# Count tokens for any model
text = "def hello(): print('world')"
tokens = count_tokens(text, "claude-3.5-sonnet")  # 10 tokens
tokens = count_tokens(text, "gpt-4o")             # 10 tokens

# Use tokenizer directly
tokenizer = Tokenizer()
input_tokens = tokenizer.count_tokens(prompt, model)

Installation

pip install costs

Quick Start

1. Initialize Configuration

costs init
# Edit .env file to add your OpenRouter API key
echo "OPENROUTER_API_KEY=YOUR_KEY" >> .env

2. Run Analysis

# Uses defaults from .env (Qwen3 Coder Next)
costs analyze --repo .

# Or specify directly
costs analyze --repo . --model openrouter/qwen/qwen3-coder-next --api-key YOUR_KEY

# Analyze all commits (not just AI-tagged)
costs analyze --repo . --all

Configuration

Create a .env file in your project root:

# Required: OpenRouter API key (https://openrouter.ai/keys)
OPENROUTER_API_KEY=YOUR_KEY
LLM_MODEL=openrouter/qwen/qwen3-coder-next

Or use the built-in init command:

costs init

Three Usage Options (Zero Config Required)

Option 1: BYOK (Bring Your Own Key) - Free

Use your own API key via OpenRouter. Costs calculated locally with real provider pricing.

# With OpenRouter key (default from .env)
costs analyze --repo .

# Explicit key
costs analyze --repo . --api-key YOUR_KEY

Supported models via liteLLM:

• openrouter/qwen/qwen3-coder-next (default)
• anthropic/claude-4-sonnet
• anthropic/claude-3.5-sonnet
• anthropic/claude-3.5-haiku
• openai/gpt-4o
• openai/gpt-4o-mini
• 100+ more via liteLLM

Option 2: Local/Ollama - Zero API Costs

No API key needed. Estimates based on diff size using local pricing.

costs --repo . --mode local

Estimation formula: diff_chars / 4 * 0.0001$/M tokens

Date Filtering

Analyze commits for specific time periods:

# Analyze specific day
costs analyze --repo . --date 2024-03-15

# Analyze date range
costs analyze --repo . --since 2024-01-01 --until 2024-03-31

# Analyze all commits since repository creation
costs analyze --repo . --full-history

# Analyze only AI-tagged commits (default)
costs analyze --repo . --ai-only

# Analyze all commits (not just AI-tagged)
costs analyze --repo . --all

Badge Generation

Generate and update cost badges in your README:

# Generate badge based on pyproject.toml configuration (AI commits only)
costs auto-badge --repo .

# Generate badge for all commits (not just AI-tagged)
costs auto-badge --repo . --all

# Manual badge generation
costs badge --repo . --model openrouter/qwen/qwen3-coder-next

# Manual badge for all commits
costs badge --repo . --all

This adds a badge section to README showing total cost, AI commits, and model used.

Report Generation

# Generate markdown report with charts
costs report --repo . --format markdown

# Generate HTML report
costs report --repo . --format html

# Generate both and update README
costs report --repo . --format both --update-readme

Python API

Use the calculator directly in your code:

from costs.calculator import ai_cost, estimate_tokens, calculate_cost

# Calculate complete cost with ROI
result = ai_cost(commit_diff, model="claude-3.5-sonnet")
print(f"Cost: {result['cost_formatted']}")
print(f"Tokens: {result['tokens']['total']}")
print(f"ROI: {result['roi_formatted']}")

# Estimate tokens only
tokens = estimate_tokens(diff, model="gpt-4o")
print(f"Input: {tokens['input']}, Output: {tokens['output']}")

# Calculate cost from tokens
cost = calculate_cost(tokens, "openrouter/qwen/qwen3-coder-next")

See examples/ directory for more usage patterns.

How It Works

1. Parse git history - Analyzes commits with optional [ai:model] tags
2. Estimate tokens - Uses tiktoken for OpenAI models, Anthropic tokenizer for Claude, with accurate diff parsing
3. Calculate cost - Multiplies tokens × model price
4. Generate ROI - Estimates time saved (100 LOC/h × $100/h)

By default, only commits with [ai:] tags are analyzed. Use --all to analyze all commits.

Why liteLLM?

• Universal API - Jedna składnia dla 100+ providerów
• Automatic routing - Fallback między providerami
• Cost tracking - Wbudowane liczenie tokenów
• OpenRouter - Dostęp do najnowszych modeli bez kont premium

Option 3: SaaS Subscription - Managed

Enterprise managed solution with dashboard and invoicing.

costs --repo . --saas-token PLACEHOLDER

Usage Examples

# Initialize .env config
costs init

# Analyze last 50 commits (uses .env defaults)
costs analyze --repo . -n 50

# Use specific model via liteLLM
costs analyze --repo . --model anthropic/claude-3.5-sonnet

# Analyze all commits (not just AI-tagged)
costs analyze --repo . --all

# Analyze with date filtering
costs analyze --repo . --since 2024-01-01 --until 2024-03-31

# Export to custom file
costs analyze --repo . --output my_costs.csv

# Show repository statistics
costs stats --repo .

# Generate reports
costs report --repo . --format both --update-readme

# Generate badge for all commits
costs badge --repo . --all

# Auto-badge with pyproject.toml config
costs auto-badge --repo . --all

# Estimate single diff
costs estimate my_changes.patch

# Read diff from stdin
git diff HEAD~1 | costs estimate -

Tagging AI Commits

Tag commits with [ai:model] for automatic tracking:

git commit -m "[ai:openrouter/qwen/qwen3-coder-next] Refactor authentication"
git commit -m "[ai:anthropic/claude-3.5-sonnet] Add payment integration"

Sample Output

🔍 Analyzing 100 commits from my-project...
🤖 Model: openrouter/qwen/qwen3-coder-next | Mode: byok

==================================================
📊 AI COST ANALYSIS - openrouter/qwen/qwen3-coder-next
==================================================
   Commits analyzed: 42
   Total cost:       $12.34
   Hours saved:      15.3h
   Value generated:  $1530.00
   ROI:              124x
==================================================
📁 Results saved to: ai_costs.csv

💡 Recent AI commits:
   a1b2c3d4 | $0.32 | [ai:qwen3-coder-next] Refactor...
   e5f6g7h8 | $0.45 | [ai:qwen3-coder-next] Add feature...

CSV Export Format

Column	Description
commit_hash	Short commit SHA
commit_message	Full commit message
author	Commit author name
date	ISO format datetime
cost	Calculated cost in USD
cost_formatted	Formatted cost string
model	AI model used
mode	Calculation mode (byok/local/saas)
tokens_input	Estimated input tokens
tokens_output	Estimated output tokens
hours_saved	Estimated hours saved
roi	ROI multiplier

Pricing Reference

Model	Input	Output	Tokenizer
anthropic/claude-4-sonnet	$3/M	$15/M	Anthropic
anthropic/claude-3.5-sonnet	$3/M	$15/M	Anthropic
anthropic/claude-3.5-haiku	$0.8/M	$4/M	Anthropic
openai/gpt-4o	$5/M	$15/M	tiktoken
openai/gpt-4o-mini	$0.15/M	$0.6/M	tiktoken
openrouter/qwen/qwen3-coder-next	$0.50/M	$1.50/M	tiktoken
ollama/*	~$0.0001/M	~$0.0001/M	tiktoken

Business Model

Tier	Price	Features
BYOK	Free	Use your own OpenRouter API key
SaaS	$9/month	Unlimited, managed keys, dashboard, EU invoicing

Development

# Install with poetry
poetry install

# Run CLI
poetry run costs analyze --repo ..

# Publish to PyPI
poetry publish --build

PHP Badge Service

Standalone PHP service for generating badges:

cd services/badge-service
composer install
php -S localhost:8080

Generate badges via API:

curl "http://localhost:8080/badge.php?cost=12.34&model=claude-4&commits=42"

Automatic Cost Calculation

The tool can automatically calculate costs and update badges on every commit and during test runs.

Pre-commit Hook

Install the pre-commit hook to automatically update the badge before each commit:

# Copy hook to git hooks
cp hooks/pre-commit .git/hooks/pre-commit
chmod +x .git/hooks/pre-commit

# Or use project.sh (includes hook installation)
bash project.sh

The hook will:

1. Detect costs in global PATH or virtualenv
2. Run costs auto-badge if [tool.costs] is configured in pyproject.toml
3. Stage updated README.md (interactive prompt in terminal)

Pytest Integration

Tests automatically validate the cost calculation pipeline:

# Run all tests including auto-badge test
pytest tests/test_cost.py -v

# Test will:
# - Check if [tool.costs] is configured
# - Run costs auto-badge
# - Verify badge was updated

GitHub Actions

The repository includes a workflow that runs on push/PR:

# .github/workflows/ai-cost-badge.yml
# Automatically updates badge on main branch

CLI Commands

Command	Description	Key Options
costs init	Initialize .env configuration	--force - overwrite existing
costs analyze	Analyze repository commits	--repo, --model, --api-key, --all, --since, --until, --date, --full-history, --max-commits, --output
costs stats	Show repository statistics	--repo
costs report	Generate markdown/HTML reports	--repo, --model, --format, --output, --update-readme
costs badge	Generate cost badge	--repo, --model, --all
costs auto-badge	Auto-generate badge from pyproject.toml	--repo, --all
costs estimate	Estimate cost for single diff	--model

📖 Automatic Badge Generation: See docs/AUTO_BADGE.md for GitHub Actions, pre-commit hooks, and CI/CD integration.

Environment Variables

Variable	Description	Default
OPENROUTER_API_KEY	OpenRouter API key	(required for BYOK)
LLM_MODEL	Default model for calculations	openrouter/qwen/qwen3-coder-next

License

Licensed under Apache-2.0.